ABSTRACT In senior living facilities, some of the most common health related problems stem from the GI tract and urinary system including Clostridium difficile Infections (CDI), Urinary Tract Infections (UTI), Constipation and Colon Cancer. The best way to lower costs and treat these conditions effectively is early diagnosis and treatment. Current clinical management for these conditions, as well as many others, include monitoring of specific excreta characteristics including urine color, urination frequency, urine duration, stool color, stool frequency and stool consistency. These logs are the best tool doctors currently have to screen for conditions such as UTIs, infectious diarrhea, dehydration, chronic kidney disease, GI bleeding, GU surgery, inflammatory bowel disease, and constipation, that can lead to hospitalizations and readmissions. However, these logs are on average 61% inaccurate at reporting adverse episodes such as diarrhea. Toi Labs has developed the patented TrueLoo technology to take pictures of excreta and, using machine learning algorithms, classify the toileting event using Digital Biomarkers (DBMs). The ability to create an excreta log to accurately deliver detailed information to doctors and healthcare providers can revolutionize healthcare by notifying when further screening (urine or fecal) is necessary. This novel, low-cost approach of machine learning and image identification technology that requires no change in behavior of the user will enable currently undetectable links between medical records and specific excreta patterns. In the future, the machine learning algorithm may be able to determine links between these excreta logs and the onset of specific diseases. In this study we will be collecting manual excreta records and automated TrueLoo digital excreta records in memory care and assisted living facilities, and compare them against each other and against patients’ deidentified medical records. We will determine correlative data between excreta logs and adverse events to establish conditional threshold for each type of adverse event and, using ML, try and establish individual conditional thresholds for reporting to caregiving staff. We will compare the manual and digital logs to assess the difference between speed of adverse episode identification when using TrueLoo as compared to manual logs.